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   "source": [
    "# Shapelets and the Shapelet Transform with sktime\n",
    "\n",
    "Introduced in [1], a shapelet is a time series subsequences that is identified as being representative of class membership. Shapelets are a powerful approach for measuring _phase-independent_ similarity between time series; they can occur at any point within a series and offer _interpretable_ results for how matches occur. The original research extracted shapelets to build a decision tree classifier. \n",
    "\n",
    "The example below illustrates how leaf shape can be represented as a one-dimensional time series (blue line) to distinguish between two species.[2]\n",
    "\n",
    "<img src = \"img/leaf_types.png\">\n",
    "<img src = \"img/verdena_shapelet.png\">\n",
    "\n",
    "The highlighted red subsection of the time series (i.e., \"subsequences\") above is the shapelet that distinguishes *Verbena urticifolia* from *Urtica dioica*.\n",
    "\n",
    "## The Shapelet Transform\n",
    "\n",
    "Much research emphasis has been placed on shapelet-based approaches for time series classification (TSC) since the original research was proposed. The current state-of-the-art for shapelets is the **shapelet transform** (ST) [3, 4]. The transform improves upon the original use of shapelets by separating shapelet extraction from the classification algorithm, allowing interpretable phase-independent classification of time series with any standard classification algorithm (such as random/rotation forest, neural networks, nearest neighbour classifications, ensembles of all, etc.). To facilitate this, rather than recursively assessing data for the best shapelet, the transform evaluates candidate shapelets in a single procedure to rank them based on information gain. Then, given a set of _k_ shapelets, a time series can be transformed into _k_ features by calculating the distance from the series to each shapelet. By transforming a dataset in this manner any vector-based classification algorithm can be applied to a shapelet-transformed time series problem while the interpretability of shapelets is maintained through the ranked list of the _best_ shapelets during transformation. \n",
    "\n",
    "Shapelets can provide interpretable results, as seen in the figure below:\n",
    "\n",
    "<img src = \"img/leaves_shapelets.png\">\n",
    "\n",
    "The shapelet has \"discovered\" where the two plant species distinctly differ. *Urtica dioica* has a stem that connects to the leaf at almost 90 degrees, whereas the stem of *Verbena urticifolia* connects to the leaf at a wider angle. \n",
    "\n",
    "Having found shapelet, its distance to the nearest matching subsequence in all objects in the database can be recorded. Finally, a simple decision tree classifier can be built to determine whether an object $Q$ has a subsequence within a certain distance from shapelet $I$.\n",
    "\n",
    "<img src = \"img/shapelet_classifier.png\">\n",
    "\n",
    "#### References\n",
    "[1] Ye, Lexiang, and Eamonn Keogh. \"Time series shapelets: a novel technique that allows accurate, interpretable and fast classification.\" Data mining and knowledge discovery 22, no. 1-2 (2011): 149-182.\n",
    "\n",
    "[2] Ye, Lexiang, and Eamonn Keogh. \"Time series shapelets: a new primitive for data mining.\" In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 947-956. 2009.\n",
    "\n",
    "[3] Lines, Jason, Luke M. Davis, Jon Hills, and Anthony Bagnall. \"A shapelet transform for time series classification.\" In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 289-297. ACM, 2012.\n",
    "\n",
    "[4] Hills, Jon, Jason Lines, Edgaras Baranauskas, James Mapp, and Anthony Bagnall. \"Classification of time series by shapelet transformation.\" Data Mining and Knowledge Discovery 28, no. 4 (2014): 851-881.\n",
    "\n",
    "[5] Bostrom, Aaron, and Anthony Bagnall. \"Binary shapelet transform for multiclass time series classification.\" In Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII, pp. 24-46. Springer, Berlin, Heidelberg, 2017.\n",
    "\n",
    "## Example: The Shapelet Transform in sktime\n",
    "\n",
    "The following workbook demonstrates a full workflow of using the shapelet transform in `sktime` with a `scikit-learn` classifier with the [OSU Leaf](http://www.timeseriesclassification.com/description.php?Dataset=OSULeaf) dataset, which consists of one dimensional outlines of six leaf classes: *Acer Circinatum*, *Acer Glabrum*, *Acer Macrophyllum*, *Acer Negundo*, *Quercus Garryana* and *Quercus Kelloggii*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:33:01.689267Z",
     "iopub.status.busy": "2020-12-19T14:33:01.688493Z",
     "iopub.status.idle": "2020-12-19T14:33:02.475052Z",
     "shell.execute_reply": "2020-12-19T14:33:02.475570Z"
    }
   },
   "outputs": [],
   "source": [
    "from sktime.datasets import load_osuleaf\n",
    "from sktime.transformations.panel.shapelets import ContractedShapeletTransform\n",
    "\n",
    "train_x, train_y = load_osuleaf(split=\"train\", return_X_y=True)\n",
    "test_x, test_y = load_osuleaf(split=\"test\", return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:33:02.479872Z",
     "iopub.status.busy": "2020-12-19T14:33:02.479312Z",
     "iopub.status.idle": "2020-12-19T14:34:04.232160Z",
     "shell.execute_reply": "2020-12-19T14:34:04.232679Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visiting series: 51 (#1)\n",
      "2.5714950561523438\n",
      "Candidate finished. 00:57 remaining\n",
      "Candidate rejected. 00:-4 remaining\n",
      "2.962433099746704\n",
      "Candidate rejected. 00:-6 remaining\n",
      "3.0671868324279785\n",
      "Candidate finished. 00:48 remaining\n",
      "Candidate finished. 00:46 remaining\n",
      "Candidate finished. 00:43 remaining\n",
      "3.130671262741089\n",
      "Candidate finished. 00:40 remaining\n",
      "Candidate finished. 00:38 remaining\n",
      "Candidate finished. 00:36 remaining\n",
      "Candidate finished. 00:33 remaining\n",
      "visiting series: 55 (#2)\n",
      "Candidate finished. 00:31 remaining\n",
      "3.400134801864624\n",
      "Candidate finished. 00:27 remaining\n",
      "Candidate finished. 00:25 remaining\n",
      "Candidate finished. 00:24 remaining\n",
      "Candidate rejected. 00:-36 remaining\n",
      "Candidate finished. 00:19 remaining\n",
      "Candidate finished. 00:17 remaining\n",
      "Candidate finished. 00:15 remaining\n",
      "Candidate finished. 00:13 remaining\n",
      "Candidate finished. 00:11 remaining\n",
      "visiting series: 4 (#3)\n",
      "Candidate finished. 00:08 remaining\n",
      "Candidate finished. 00:06 remaining\n",
      "Candidate finished. 00:03 remaining\n",
      "Candidate finished. 00:00 remaining\n",
      "No more time available! It's been 01:01\n",
      "Stopping search\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ContractedShapeletTransform(num_candidates_to_sample_per_case=10,\n",
       "                            time_contract_in_mins=1, verbose=2)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How long (in minutes) to extract shapelets for.\n",
    "# This is a simple lower-bound initially;\n",
    "# once time is up, no further shapelets will be assessed\n",
    "time_contract_in_mins = 1\n",
    "\n",
    "# The initial number of shapelet candidates to assess per training series.\n",
    "# If all series are visited and time remains on the contract then another\n",
    "# pass of the data will occur\n",
    "initial_num_shapelets_per_case = 10\n",
    "\n",
    "# Whether or not to print on-going information about shapelet extraction.\n",
    "# Useful for demo/debugging\n",
    "verbose = 2\n",
    "\n",
    "st = ContractedShapeletTransform(\n",
    "    time_contract_in_mins=time_contract_in_mins,\n",
    "    num_candidates_to_sample_per_case=initial_num_shapelets_per_case,\n",
    "    verbose=verbose,\n",
    ")\n",
    "st.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:34:04.238538Z",
     "iopub.status.busy": "2020-12-19T14:34:04.238006Z",
     "iopub.status.idle": "2020-12-19T14:34:04.898056Z",
     "shell.execute_reply": "2020-12-19T14:34:04.898848Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series ID: 51, start_pos: 109, length: 68, info_gain: 0.14713002000707054, \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series ID: 55, start_pos: 75, length: 314, info_gain: 0.14402946691987872, \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series ID: 51, start_pos: 299, length: 92, info_gain: 0.11488133254372052, \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series ID: 51, start_pos: 30, length: 46, info_gain: 0.09575722605459835, \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series ID: 4, start_pos: 311, length: 94, info_gain: 0.09097204859324504, \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for each extracted shapelet (in descending order of quality/information gain)\n",
    "for s in st.shapelets[0:5]:\n",
    "\n",
    "    # summary info about the shapelet\n",
    "    print(s)\n",
    "\n",
    "    # plot the series that the shapelet was extracted from\n",
    "    plt.plot(train_x.iloc[s.series_id, 0], \"gray\")\n",
    "\n",
    "    # overlay the shapelet onto the full series\n",
    "    plt.plot(\n",
    "        list(range(s.start_pos, (s.start_pos + s.length))),\n",
    "        train_x.iloc[s.series_id, 0][s.start_pos : s.start_pos + s.length],\n",
    "        \"r\",\n",
    "        linewidth=3.0,\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:34:04.936596Z",
     "iopub.status.busy": "2020-12-19T14:34:04.930211Z",
     "iopub.status.idle": "2020-12-19T14:34:05.005915Z",
     "shell.execute_reply": "2020-12-19T14:34:05.006458Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#0: Series ID: 51, start_pos: 109, length: 68, info_gain: 0.14713002000707054, \n",
      "#1: Series ID: 55, start_pos: 75, length: 314, info_gain: 0.14402946691987872, \n",
      "#2: Series ID: 51, start_pos: 299, length: 92, info_gain: 0.11488133254372052, \n",
      "#3: Series ID: 51, start_pos: 30, length: 46, info_gain: 0.09575722605459835, \n",
      "#4: Series ID: 4, start_pos: 311, length: 94, info_gain: 0.09097204859324504, \n"
     ]
    },
    {
     "data": {
      "image/png": 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oM2JjYQZ+8RGw1cKdX4aibXDl2+HMT5ebU3X8Fir2xGWC+JjTxcjMAs2RLnT6aTgMSJ2GuBXxLMCpH8LOuyG3jKnaa1kwmSjpPaackkVqc2vpme5h3O6ksCI76BNf2eKC51DXlIqPV+6Ly2d9I9BCHiPS7cYzPIy1oiLRpsTGE38N491w7z9D+qKYNt0G84vNqeYmVIZI021xufyFQRWb3FkRQerhSqquAWv2cnhlrBO+fy889UVwzxlrpCa5uPBr9SR29fsBlbECUJJRCA98WJXXAzW5NfRO9TI+OEtBefDWD/klmaRnWRhqG4G+YykbHwct5DHjHnKAlFgrUnChs+s59Yh6/Ueh/uDydv9jZcdvlVBKb9yE/LxdpX3tiCSHfCWWNBXq6XhG/Tu+fYfypo58Rf083mW8sZrk4MT3Ib8WGm4GYGh2CIDyQ5+F6SH45UcBqM+vxzXrZd7pDph66EeYBEVVOYx2OtRnPUXj46CFPGY8g2pR0JJqHrmUanEzrxpu/dzqfbllULEXzvwMWh+H9HyovjYuZlywT1Gck0ZJpBkrK9l2C4y2wvferMqp//gleM+P1RPGd++G+anw59CkFuNdysG4+n1gUrLlF/Ky+sNw0yfg4sMw0kazrZn8eZWSaCsN3cOnqCqHsWEf0pINNfH5rG8EWshjxG1XQm6tSLEYeetjqnH+4U+BNcCHfP8HYeiMymJpPBy3UuVz9qnovXE/+z8Ehz4N138M7nsaSnfA9jvhvT+FqX546m+NNVaTeE7+BwgTXPWepU1DziEEQuWRX/P7KjX12LdpLlgh5CE8coCiqmzcXgtT5XeBJXqn4pXBV3i089HgQyo2CC3kMeIe8At5CoVWpISnv6hmEV71e4GP2f0OSPPHzOPTynPM6eKcfYoD9TEuLFkz4Na/hDv+FtJXFBPVXAv73gfHvwuT/YbYqkkCfD549YeqtWz+cpbY0OwQRZlFWM1W9TS56y1w8gcUmNKo8jQghSSvOIxHnq/WVUZzYmvR/OOLP+arx78aeQptnNBCHiPuQTvm/HxMmWHaryYT/cfVQubBPwNzkNz3tGz46EvqUfXKt8XFjGdbh5ESDrfEoQvmjf9dvX7rZjhxv/rlpUltBk6oHii7375q8+DsIGVZZcsbDvyR6pt/7peUe2qZy5zCbAktcUWu46QJJ2d6GmPyqi+OX2R74fao32c0WshjxGMfTL34+KkfgSUjvEDnV8Pr/+dqb9dAjlwawZZlZXdVvvEnL2yADz6qyqwf/Bg89jlwz4d/nyZ5OfcLFTbZvnoI2ZBzaLWQ114HOeXQ9gQFC2WMpPUvdUcMhnXgOa61PUBvhxt7W4iBzAGY98zTPdVNS0FLVO+LB1rIY8Q9OJhapflet1rE3PEmyIgxNm0QL3WOcn1jEeZwPchjpeoa+INHVLz/hW/ANw7oMXGpipRw7kG1XnPZ6LUh5xBl2SuEXAhovBnZ/ltMkxlMZDj4TddvQp+/6yg7drpBQN+F6IactU204ZM+dhTuiOp98UALeYx4BgexlJeFPzBZaHsS5sZg9+8m1Iz+iTn6xue4tiHOhRcmE9z9VXj/g2oR6wfv0MObU5HB0zDRDbvuWbV5bH6Mafc01TmXVVY33szsjA+vS5JZZOZo/9Hg554ehLF20rcdoKQml4HWiahMuzR+CYDtBTq0kpL45ubwTkxgLU+h0MprP4bMwrgtYEbK820jAPEXcj+Nh+GDvwFzGrzwTxtzTY1xnPm5KslvedOqze0T7QA02ZpWH990GxNeJe6VVcWcHT2Lx+cJfO6u59Rr/UEqm20MdkzhdfsCHxuAvuk+zMJMVU5VxO+JF1rIY8AzpPJXU8YjX5iGCw/DFW8Nvsi5Acy5vHz9qTYai7NjTz2Mhewi2PsuOPsAOEc27rqa9eHzqgZZTa9f00a5bUL1V2m0XdZyNqeEiTxV5NbS0MCcZ27Jc15D91FIy4XyvZTU5uL1+Jgcjrw6eGh2iJKsEswmc+T/pjihhTwG3IODAKnjkV94SHUx3JPYsMpPjvfSMzbL/7r3yvjFx4Ox/4Oql/nJ/9jY62pip/uoqgvY+641u9on2smx5qxe7FxkLGM/FjHHNcWqK+Kp4SAhta6jUPs6MFuW8s0nHJEPLRl0DlKelRzrZFrIY8Bt9wt5injkp3+smmPVvC5hJkgpuf+FbvZU53NDU/HGG1C6E+oOwvF/1ymJqcL5X4ElE7a/Yc2ujskOGm2NAfO3RxaqKLL0UNVzDFu6ja+f/Dp7vreHyYUVWSkzwzByUX0mWC4c8g9rjoRB5yDl2VrIUxa3fQBIkfL8GYcqbd79DrWqnyDO26dpdczwrgMJnKa0772q1Lv/eOJs0ESGlCocuO1WSFtbndk/3U9N7tpZuVJKRoc8FOVOIS4+TEthC9OuaSSSc6Pnlg/sXlwErb8RgPRMC5l5aRELuZSSodkhLeSpjLu/H0tJCab0GPqEbDSn/0s1BEpwtsqRVtWp7tYdpWGOjCMtb1SLnmcfSJwNmwif18eZZ/roPjtq/Mkd51QRUMtab9wnfThmHQHDGjPjCyzMeiiuL4beF9mRs+w4tI63Lh/YfRSsWap17SK20syIQyvjC+MseBe0kKcy7v4BrFWJX6kOi5SqurH6gOpHkkCOXBqmpSyX8vyMxBmRaVMd7s4/qMMrBiBMgpOP93DmmTi0Q/Dn/TeuLZ0fnRvFIz0BRXS0X82BLdp7FUgf5TPLv2Qujl9cPrDrqGrpsGLxv7Aim7EBZ0QVnoNOFV7VMfIUxt3fnxpC3veKigPue19CzZiYdfFy5xg374hDSX60bL8TJnpgpDX8sZqQCCGou7KYvvNjeFxeY0/e+SwUNKi1nctYEtEAQj7Styjku/dAfi23DvdgS7dRnFnMhbHFmbSzY+A4C3U3rnpvSW0uC7MepkbCVwL70x/r8uqi+mfFCy3kUSK9XjUZKBWE/MT31RCGK38noWY8emYQj0/y5j1J0Cmy+Q712vpYYu3YJNTvLsLj9tF30cDRe163yvEOMvR7qX1tgIyV0f4ZcosySM+yws67qew4yrNvfZi7G++ma7ILn/QtTxNa2YsfKK1TKbGO7vBtkM+NniPDnEFDfkM0/7K4oYU8SjwOB3g8yS/kCzMqFnzFW+PWMyVSfvFqPw3F2VxRmdjWAADYaqB0F7SGKd3WRETldhsms8DeNmHcSbufV82vmu8MuDuURz7aN0NR1WL3zh1vUnNo256kJrcGl8+lpgp1PK0cnKr9q95bWJmNySIY7p7mzDN9/PhLrzDvdAe04fzYeVoKW5Iihxy0kEeNu1/FA5NeyM8+AK4Z1Yg/gXSOOHmxY4y3X1Od8FafSzTfDt0v6AEUBmCxmimuzmGo08B7efFh1dwtyOi1odkh0s3p2NJtq7Z73F4mhmYprl4U8prrVDXzhV8vlfL3TveqQcv1B9W0qRWYLSaKq3JoPTbEMz+8xHDPNF2vrS0g80kfF8cusrNw5/r/rQahhTxKloU8CcIEoTh5PxQ1JzR3HOBnx/swmwTvuKY6/MEbRfMd4HMvz/3UrIuy+jwc3dP4fAYtILc9AfU3qZbKAeib7qMyp3KNYzAxNIeUatESUENRWt4Ilx6jejEM0+c4BWPtQce6FVXlMDO+sPT3rtNrhXx4dpgZ9wzNBc2x/OvighbyKHH5hbwyiYV8+KIanHz1+xOaOw7w9EUH19QVUJqXwGyVy6l5nRpjp+PkhlDWkId7wcu43bn+k03ZYbRteX5sALqmuqjPq1+z3Z8Dvmoq0I43wcIkFSPtmISJvr4X1fYg3r4/LGNNN7PzYAU958bweVf3XxlwqjqSypzk0QAt5FGSEjnkJ+8HkwX2vjuhZjim5zk7MMXh7UmQrbISs1V9kVsf12mIBlBa718knF7/yfyNrIIsdHp9XrqnuqnPr1+zb2JI/SJZJeTbbgFrFtaLj1KeVU7v2CXOFlTiLQrsTRdWKW++uDqH2l1FuOe9a/5d/TPKmdNCnsK4+weS2xv3utUAie13QU5iBfSZi6oIKOmEHFR4ZdoOg68l2pKUJ780C0u6meFeI4T8iBqoXb4n4O6BmQHcPjcNeWuzRcaHZskpSMeavmIB0pqpmm5deIiGvHqec4/yLpuFBzt+FfD8xYseeXFNLlUtNhDQe351n3L7jBrzWJGdPJXdWsijxD2Q5MVAlx4F57AKqySYJ84PUZ6XkRzZKpfTdJt61eGVdWMyCUqqcxjpMUDIO59V/U+CZIN0TnUCBPbIB2cDD1vedS/MDLJ3zsnUYrO2VwZfCXj+zNw0bnrndnbfXEVmThrF1Tn0XVidWtk/009hRiGZluQZ86iFPApSIof8xP2QW6EG1SaQebeXI5dGuG1XafJkq6wkt0yVZ7c+nmhLNgUltbkM982sb8Fzsg/GO5f6nwSie6obYE2MXPok40OzFAQS8p1vgbwq9p5f/r8+4TgR9Bp7bqmmoFyFWKp3FDLYObmq4GlgZiApepCvRAt5FHiGh8HtTt6MlSk7tD0OV71HrdgnkLMDk8y5vRzensDeKuFovhP6XobJOJSYbzFK63LxrHfBc2nQQ+D4OEDPVA+5ablrUg+nRudxz3sp8qcersSSBrd9nj0LC5ikxGqy0j/Tz5BzKKxJVdtt+DwSe8dy50S7055UYRUwSMiFEHcJIS4KIdqEEJ8x4pzJiHtArVYnbYz87AMgfQlf5AS4OKhKpXdWJLYYKSRXvUe9Hvt2Yu3YBJRvU4O07e3RDTBeReezkFkAZVcGPaRnuofa3No1T3kji/H5ktogn7c9v0vOBx7iK3s/zl9d91fA8nCKUFQ22xAmQf9i5apP+janRy6EMAP/BLwB2AW8Wwixa73nTUbc/cku5D+H8t1QHJ/81u8e7eQD33k5omMvDU2TnWamypY8ccQ1FNSpPOOX/03139DETF5xJpm5VoY61iHkXUcW4+PBZalnSgn55Yz0zSBMYjmHPBD1B7lz333cVK08/q6prrAmpWVYVhU8jc6N4vK5kipjBYzxyK8F2qSUHVJKF/Aj4J4w70lJktojH+9STbKufFtcTj897+bzvzrHM5eGmVkIMgNxBRcHp2kuy03O+PhKbvlLcE3Dk3+TaEtSGiEE5Y35DHZO0TXZxe7v7ebsyNnITzDerZqZhQiruL1uBpwD1OSt7UM+0jdDQXkWlrTwJfNFGUXkWHPomuyKyLSy+jwcXVNIn0zK1EMwRsirgN4Vf+9b3LYKIcR9QohjQohjw8PD67qg9HoZ+ea/qJj1BuIeGMBss2HKCrCgkmj8PbaviE+DrEfODC793D0aOg4qpeTS0DQtZUkcVvFTtguu+2M1OejpL6s5kZqYKK3PY2JolkcuqkygXwVJ8QtImPxxUIU4PukL6JGP251LC5ThEEJQn1cfkUcOquDJNe9lfHCWgZnFYqDszSfkgVyuNUvXUspvSSn3Syn3l5SsL6944eJFhr/2Nbre+951nSda3P39yemNA5z5mWoCVBCftpqv9S0/MveMhm6+b5+cZ9TpYlcyph0G4vV/rZ5knvk7OPp/E21NylJap35xT/SpNrAioDQEoetZyCqCkuD9S/wx7ctTD70eH1Oj8xSUR+5g1edHJ+QA/ZfGk7KqE4wR8j5g5bNONTBgwHmD4h9+7O7uoefDH0a6XPG83PJ1BwaSM2NlpFUVtsQprAIqC2VnhfpAd4UR8tN9EwDsrbHFzR5DsaTB278DO+6GI3+v0uA0UeNvAzvWoz4fvdO9oQ5fRkq10Fl/Y8j4+JmRM1iEhZaCllXbJ4fnkD4ZOIc8CA35DQw6B3G6w2fZ2MqyKKrK5sILdgZmBihILyDLunyts8/2c+mVwRBniD9GCPkrQLMQokEIkQa8C3jQgPMGxT2gKqvy77kH5zNHGP3e9+J5OUCFC9x2e3J65Gd+Dgi44t64nN7rk5y3T/O6hkKKstPoGQv94X+1dxKrWSR3xkog7vqyEpVHP5toS1KSjGwreSUZMKwWuP0532EZ71Jj3ULEx0EJeXNBMxmW1X17AvZYCUOTrQmILHNFCMHOg5U4uqex946tmhXq8/r47Q8u8vi3zzE7tTEOZSDWLeRSSg/wMeA3wHngx1LKKFY5osdtH0CkpVHx5S+RfcMNTPzov+J5OQC8ExPIubnkFPLzv1KNoPLiY1vH8Axzbi9XVOZRW5RFdwiPXErJC+0j7KzII92SHL2aI8ZWC4c+oUbBdejOiLGQW2mlcKYSW7qNvpk+vJGsOXQ9q15DCLmUkrOjZ7mi+Io1+/xCHrAYKAj+zoVt4+GFHKDxKhUOnusyrbJhZfve136buCc5Q/LIpZQPSym3Sym3SSm/aMQ5Q+Gx27FUlCNMJnIOH8Ld3497KHxy/3rwZ6xYkk3Ix7th6DXYeXfcLvFih5p7eKC+kLLcDBzTC0GP/cWr/Zzqm+R396/NLEgJrv8TJehPfF431IoBUbJA3kIRB4sO4fF5lrI8QtL5LGSXQklL0EN6pnuYdk1zZdHaHPPxoVky89JIy4y8CK4qp4pMSyatE5GN/MstzCCr2ELpWAO7i3cv23VuDGESlNTm0nbcEdG8z3iQkpWd7gE71golqJlXXw3A3IngJbfGXHMx9bAiyYT84sPqteWNcbvE0bZRqmyZ1BVlUZybxshMcCH/3vPdtJTl8p5r12YWpATWDDj4ZzBwYnkkmCZi5m0TABywKO961cDjQEipPPL6G0O2XH5tRDU3u7J4rZBPBivND4FJmGiyNXFp/FLkb6pyUjnVxM785TKZ4Z5pCiuz2XlDBRNDs4wZ0co3BlJTyAcHsVaoEtmMHTsQmZk4n4/vl85jV3H5pFvsvPCQGl1WtC0up/f5JC92jnKwqQghBMU56UzMunFf1qMZoHdslld7J7h3XxUmU5Lnj4di77tVBsUL30i0JSnHaPZiVsd8IyZh4uJYGCEf61BdKEOkHYKKj2daMtlmW/s5n3DMYiuNvvBsb8leTg+fZt4TftgywHhZL1ZfOqaB5bWf8UEnheVZNO4rwWQSnD9qj9oOI0g5IZduNx6HY0nIhdVK/pvfzMQDv2Dipz/FNxs6oyJW3P0DiIwMzDZbXM4fE7Nj0H1UNc+PE52jTiZm3eyvLwSgOEf1YR+dWbuw8/RFBwBvuHLtLMWUIi0LDvwhXHxEZQRpIsbu62UufYoZu5v6vPrwQt55RL1GsNC5s3AnFtPq8Mm8083ctBtbWWQ55Cu5sepGFrwLHBs6RsdER9jj+/Iv4rbM03lSTQ3yuLwq7bEim+z8dBqvLuH883bcro2vRUg5IXcPDYHPt8ozLvn4n2ApLcH+ub+i4y334JuP7DdsVNddzFhJqkrF1sdUb5WWN8TtEqd6JwC4ajGV0C/kgcIrFwanyc+0UleUhAVT0XLgj9QAipf/NdGWpARen5fJhUkGZgaYz59ktH+GloKW8KGVrmchpxyKmkIe1jHREXC02qRjDgBbWfQe+TVl15BuTuevn/9r7vnlPRzpOxLy+IG5fmYqB+k8PYLP62N8aBYkS4VIO6+vwDXnwd46wWDH5KqRcfEm9YTc3++kYrn7mKW4mKYnnqDii1/E3dfH3MmTcbhuf/K1r734iPoSVOyL2yVO902SlWZmW4nqKleSqwbWBhLytqEZmktzkuuXXazklKg+1qd+CAszibYm6fn6ya9z449upHW8FXOJm3H7LPU5jQw6B1nwBhE0f/54w00h4+NTrimm3dNLA5RX4o9JR5N66CfDksH2gu04ZtWTZOt46Kcvu9OOqcHJgtODvW2S8UF1bX8hUkWTDZNJ0Hbcwc++cpxfff3VqG2KldQT8iD9ToTJRO6dd4LZjPOll+Jy3aRKPfS4oO1J2H5HyCKK9XKyd4Irq/IxL8a8lz3y1aEVKSWXHNM0lwVoI5qqXP0+WJiC9qcSbUlseN3w4jfhmzfC16+BF/4feMP3yYmFn7f+HIDxhXEKq7Lx+SSlrmokcqmsfQ0jreB0hA2r9E+rzJeq3LWO1GDHJOlZFmylsT0FNuQvTxpy+YLngc955hibH6OwOR2zxUTnqRFG+5yYzGLp2tZ0MyV1uZx/XsXJxwaceAOsJcWDFBRy9Z9qqVjbD9ick03GlVcw+6KxQu6bncU7Pp5cQt59VDV72h6/sIpzwcPZ/kkO1BcsbQsWWhl1upiYddNcmmJFQKGovX5xSPNvEm1JbDz9JXj0L8BkVel9v/ksvPhPcbnUyiKd+gb13cydKgbg+YHnmXEFeKrpXMzVDzFIApZnZAZqHWtvn6SsIR8R4+L6SiH3j3ALhN2p9lUVVFBan8tQ1xTDvSpjxWxdltH63UUA5BWr+2FvW0c3yChIQSEfwFxSHHT4cfb11zP32mt4J427gUtPAckUWrn0KFgyoPHmuF3iRM84Hp/k2oaipW1ZaWYyrWaGL8slv2BX/aC3p0KjrEgxW6Hp1tQc0jx0Fp7/R9j7HrjvafiDh6H2Bjjx/bj8W1aGT67Y1oTFakKMqbj13738d3z6yKfXvqn9aZWzX9gY8tzBhHze6Wbc7qRisRd6LKycNBT0yYHVczqLq3IY7Z/B0T21pv/5NXfVc9//Pcw7P3ctCBhonYjZtmhISSEP5RnnHDoMXq+h6YhJ2b629TH1SJoWv4XFlzrGMJsE19Qte+RCCMrzMxicXL2gfKJnHCFgT03sX6qkpPkOmBkC+6lEWxI5Ph/86s8gPQ/u+F9qmxCw7/dgtE21OzaQadc0Y/Oqn3uWJYuq3CoKK7OZHVzO3jg2dGz1mzwu5ZE33RYyPg7QN91HrjWX/PTVn63hxRmh/qZWsbDSI/c3xAqE3yOvzKmkuCYX94KXBaeH0suEXJgE1nQzaRkWCsqylmyMNykn5AttbaQ3BP8Nnrl3D2abjZkjzxp2TXe/8giSxiOf7FP5t9tuietlXu4c48rKPHLSV6d8Vdky6ZuYW7XtePc4LWW55GVY42rThtN0OyBSa0jz8X9XI+zu/BJkLz9NsesesGbBqz8w9HI9Uz0A3N14N/ftuU/VG1TnMNrnXOqDWpJ5WcfT3pfANbM8BDsE/TP9AePjI30qXFNcE/u6TH1ePe9seSeHqw8z6BzEJwPHtAdmBjALM6VZpavGyVVuLwh4PEBJXS6O7qmg+40kpYTcMzKCd3iE9B3BS3mF2Uzm3r3Mnzlj2HXdAwNgtWIpKV69Q0p47mswdM6wa0VE5+IvqYZDcbvEgsfLq30THFjMH19JlS2T/vFlIff6JCd6xrm6LviHOmXJKYGqq+FSisTJpwfhiS9Aw2HY+67V+9JzlZif+Tm45wK/PwY6JlUO9h/u/kM+tPtDABTX5DLvdPPpnf+DDHPGWpFsewJMlog+w/0z/QHj4yN902Tnp5GZkxaz7WaTmc9d9zlurrkZt88dtKWA3WmnNKsUi8lCUVU2eSWZ3PC2ppATiUpr85iddOGciH8aYkoJ+fwFlZOasWNHyOPSd+xgobMTn0Htbd0DdqzlqrfLKubG4aVvwvfeDH3HDblWRHQ9C5mFULq2gZBRnO6bxOXxcaAhgJAXZDIys8C8Wz06/+bsINPzHg5vX1+f+aSl+Q7oPw7O0URbEp5H/gI883D3VwOHLK56j8rEufCQYZdsn2jHYrJQm7fclsEf7rjOdCufOvApXD7XUpqfetOTUHOd+uUSAill0BmZo30zFNcYsyazvWA7QNCS/ZUDly1WM+/72+vZd3voNhQli/3ZHRsQXkkpIV+4eAGA9JbgHjlAxo4W8Hhwtbcbcl233b4qb32JrEL4/YfU4+p37oRXggzxdY4YO6m981moDz3bcL283KlinsE8coCBxfDKvx/tpL4oi9t2lsXNnoTSfDsgkz8Nse1JOPcLOPSp4C0b6m6E/FpDwyvtE+3U59VjNS2H1Yqrc7Ckm7G3T1Kdq/K/l/qTzwyr/vlNt4Y99+j8KPPe+TVC7vX6GLfPUlRlTLprk60JgQgu5DN2KnICaEAISmpyEQKOP9LFSw92xLXNbUoJuaurG0tZGZaC0I/wfqH3e/DrJeQCa9E2+PAzKnvkoT9f/WWXUg0q+D8t8NVd8B9vh3PrbNU+3gWTPVAfv7AKwNG2EXaU51KYvfaxtapACXn/xBxen+RU3yS37ypbyjXfdFTsU+l7r/0k0ZYER0r47ZeVSB/80+DHmUyw+22qTe+8MZld7ZPta3qgmMwmyurzsLdPUJenplZ1TnaqnT2LiQhh8sdhOWPF/8vAz/TIPD6fjGoqUCiyrFnU5tUGLAry+DwMzQ5FPd7Nmm7GVp7NUOcUxx7u4siPjNGjQKSUkJf/zRdo/OUvwh6XVleHyMhg4cKFdV9zqbdLZYjfxlmF8M77oXg7/Oi9y2L96Gfgqf+l4pKHP6MyH378PlWRGStL8fHwX4JYmXd7OdY9zsGm4oD7/R553/gcfeOzuDw+mko3USHQ5ZhMcO19Kp88WbNXLj2qslFu/FM18SgUzXeA9ELHb9d92XnPPH3TfWzLX/sEULEtn9F+J8XWUrKt2cvebvcLYMmEiqvCnn+pGOgyj3zCEf0wiXBsL9gesDfM8OwwXumlPDv2HkJmq4mBtsm4tblNKSEXQkTUtEqYzaQ3NzN/af2/Ad1DDvD5AhYgrcKaCe97AEp3KrH+3/Uqfn7dH8Pbvg23fBb+7DUo3wMPfhzmJmIzqOtZyC6BktDrBOvhRM84Lo+PG7YVBdxfacskzWyia9RJ+7DKHNjUQg7wuvsgLQde+pdEW7KWhRl45NPqM3H1B8IfX33tYqHT4+u+dOdkJxJJo21tJllpfR7SJxnrdbK9YPuykPc8D9X7w//CgaBT6/3DJPJj6HoYjCZbE30zfWu6IfbNqIERlz8VRMJN72ymYW8x1791G3NTLqZHje8DBSkm5NGQsaOFhQsX1/0b0GOPog95fjW857/USnzDYbj1r1Qer3/RyZoBb/k6zI6oqrtoWZptGLo3xXo51qVywvcHiI8DmE2CuqIsOoedtDmUkPt7sWxaMvJhz++qIddz44m2ZjWP/xVM9MLdX1NFTOEwW2DbzSqmvs7vhz9jJZBH7h/GPNQ1tSTkvim7eqppOBzR+XumeyjJLCHTslqwJxxzpGdZyMg2Lt210daIT/rWjKjrm1ZCvnLEW6TU7CjkjR/ZQ2WzDVAtBeLBphXy9O0teMfH8TiG13Uet78PeajQykqyi+EDv4Lf/R4c+iSYLht3VnkV7P8QvPKvMPBqdMaMdcD0QFzDKgCvdI3RUpZLfmbwL0lDcTadI0rIi3PSsGXFngKWMux9t8oIMSAkYRhtT8Kx78D1H4W66yN/X9Pt6rM0tL6pjO0T7ZiFeSkOvpLs/HRyCtMZ6pqipbAFp9tJ/7mf8FpaGgO110R0/p6pnlXZMH4mHbPYyrIMbdDm/2XUPrE6SaJ3uheLsCxlrcRCUWU21gxz3Co9N62QZ+xUoYe548fCHBkat11Nx7aWG9hj+9bPqcEFD31CVeFFSs8L6rUudG+K9eDx+jjZEzh/fCUNJdl0j85y3j69ufqrhKLiKtUWoW99nynD8LpVSKWoWT39RYO/EKdtfeGVzslOanJrsAZ5EqhozMfeOkFjngq9dLY/znuqynn7C5+L6Pw90z3U5q4V8rEBZ9RTgcJRl1eHWZjXDGTune6lIqdiTS/0aDCZTVQ12+i7EJ+nuU0r5Jl795JWV8fIP38T6Y290btnaBBTfj6mLAM/NJk2FXLpPwYn74/8fX2vqEf8ML2b18N5+zQzCx7214fODGoszsbl9fFa/yQt5VtEyC1pSsx7X060JYqT96uS+zv+VoXtoiGvQtUhtD8d06XH5sc46ThJ+2Q7jfnBK60rtxfgnHRR6FKO0Ivj5wGYdofPrXa6nYzMjazxyOdmXMxOuSg0KPXQT5o5jZrcmoAeeaAWutFSvaOQyeE5pkaNK8bys2mFXFitFN33Ryy0trJwMfZFT7d90Fhv3M+ed0LdQXjiryMvNOk7BlX745Y//uWHz/PmbzwHwLUBCoFWcmXVct+LHVtFyAFqDoD9VfBs3NCAgLic8Nu/Ux0at98V2zm23QI9L8ZU5fmh33yI9z/yfnqmegIudPqpblEOgbMLss3p/CZEuO5y/Hnnl3vkY/2qD3hRVfRTgcLRZGtaivuDKkjqme6JKT5+OdU7CjBbTIwNGD/Xc9MKOUDWNSoON3c29jigezBOQi4EvPH/g/kpePIL4Y9fmAHHOag+YLwti/zLkeUPcEV+6GyAXRXLjYq2jEcOKuPD6wL76cTa8dxXVTOv274Q+8J3483gXVBiHiX+8INXegPO0fSTX5pJZl6ayicXGTgsFtV/RQo8vtC90f09XC73yEcH1AK7UcVAK2kqaKJnumepm6PdaWfaNU1LYegixEgorMzmD//hJup3B07rXQ+bWsittbWYcnKYX4eQe+x2LBVxmkFZtkvlJ5+8H0baQh87cFKNdaveHxdTVvYXv+eq8Bk6QghublEl+ZuqdW04/L9I+xIYXhnrhKP/CLvfAbWvi/08dTeoXuUd0YVXZt2r5+IGyljxI4SgvCGPoc4pbB4XufNFvO/437C/9y5G5kZCXmepGOiysMZov5P0bAtZecYvsG+zbcMnfUvFS+dHVShoZ+HOdZ9bCIElzRz+wBjY1EIuTCYydu1i/mxsTa18c3N4Jyawlse+Wh2Wmz6hFtCe+bvQx/lbj1ZFttofDf0Tcxz6ivoyf+f39/P3b98b0fu++d5reOy/HyI7PfZFoJQjrwLyawxvBbsGxwWVHeMLsL7zm79UDadu/5v1XSMtG2peF3UWztnRZcdIIKjPrw95fFlDHpOOOa4dd3Ow63fIdudz5eBNS61hg9E33Yct3UZO2mrPe6RvhuLq+IwUbLapuaD+Cs9zY+cwC3PAeaHJxKYWcoCs/fuZP3MG58vRe1BRpx7GQk6J8spf+yk4zgc/ru+YWuTMCh27joVfnOxndnHy93WNRaRZIvtYZFjNW8sb91N9AHrjKOTdL8A/Xw/fvwf+/Q3gXlFE0vYEXHwIDn8K8gzoj994swoTRdEQ7NSwqm4tzy6nMqdyTY735ZQ1qPWUWwYL2O5U2WTp3kz6RsII+UzfmopOn08y1j9DcXV8Pne1ebVYTJal0NH50fM05DesmoCUjGx6IS/60Aex1tQw9KUvR/1eV4+K0Vlr1r/QEZKDf6qqBn8bxCuXUnmAVfEJq7zcOUZFfgZPfuIwWWlbyLuOleoDMNUHU8EHEayL4/8Owgx3fFH17X7gw6rx2kQP/PJjULhNVQwbQePNgFweuxYBp4dPU59Xzyf3f5L/tve/hT2+rD4Pk0lyce71uFwWGq4uQmCirz30L4/+mf411ZSTjlk8bh/F1fEpQLOarDTkNywJee9076rhE8nKphdyU3Y2he9/PwsXLjB/MXBns2C4e1VFV1q8hTyrEK77iOpcF2gRbbRdDamtvc7wS3u8Po51jfH6naWbvzrTKGquVa/xSEOcn4Rzv1SDn2/4mMoPP/8r+Md98M2bVLbKO+8HS+BRh1FTuU+V60cYXpFScnr4NHtK9nBn/Z3c23Rv2PdY082U2cboWFAFS1ffVo9P+BjvCl6u7vV5lZBfFh83YphEOJpsTbSOtyKlZNA5uK5CoI1i0ws5QN6b3ggWC1OPPBzV+9x9vYisLMxFgXuOGMr1H1U54oFK97tVSmC4IbWx8JPjfThdXm5q3qS9xONB+R4wp8cnTn7mZ6p6dN/71N8PfRI+8jzsfDM0HoY/fALKDOxDb7aoSuH2pyMq17c77YzOj7K7eHfk15CSKtS9Ssu0UFqfx3zBGPQHTx8cnhvG4/OsmQw0NuBECCgoMz710M/2gu3YnXZ6p3uZ986v6fOSjGwJIbcUFJBWVxd1f3JXTy9p1dVxWVRZQ6YNbvgTuPTI2iEV3c+rNqoGFwK1Dk3zpYfO87qGQu7YtUl7iccDS5qaGtR91Phzn/yBKtSp3Le8rXQH3Pv/4He/DyXrT4NbQ/PtqjXyUPipWv5sjiZbFJ/F0z+mxfQgtTXz/M4nr8ZkElhq3OROljA/F7hHt38h9HJveNzuJL80a9XkeqPx/9uO9B0JaEMysiWEHFR4xLUYKokUd19v/OPjK3ndf1Ol+0/97fI2KaHrqEoVM/gXyl/+4gzpVhP/8M6rNuaX1Wai4bDqlWNkA62JXlXtu/vtcW2KtoaWNwFChXDC0DOt1o0C9VYJiGsWHvoEtsYG3vyZ25dyv0ubsjFJM+fOdgZ825BzCICyrNUOxtjgrGE9yIPhr1R9wa5aYmx6j1wI8Q4hxFkhhE8IEZ+VOIOw1tTg7umJuBuilBJXXz9p1Rs4cDk9V6UjdjwNlxaH/U50q4U1g8MqU/NujneP864DtUv9xTVRsLRIaNyQby78Wr3uuse4c0ZCTomqEI2gT37PVA+ZlkyKMyMsarn0KLimVRvnFf1YmrfXIPHR0Ro4c2XQqXocrewB7vX6mHTMUlAev7AKKA/cJEy8MvjK0t+TnfV65GeA3wGOGGBLXEmrqcE3O4t3PDIPyjsxgZybCz4ZKF4c+CMVQvnN/1BNkboXp6nU3WDoZV5sH8Xrk9zYbHyV2Zager8a8df1nHHnPP8rFVYJNqYtnjQcUqGVMFOD/OXqET/Bnfoh5JSpdhQraCytYzzTwWjPbMC3Dc4Okm3NJjdtOc1wangOn1dSUBFfj9xqtlKeVc6cZ44caw55aXnh35Rg1iXkUsrzUsr4zS8yEGuNWv12L6YUhsOzmEMedqCE0VjSVNrZaCsc/64Kq2QWQMn6K8tW8kLHKJlWM1fXhm6OpQmC2aqKs4yq8JxxqF/aO99szPmipe4GVTkcJhOnZ6on8rDKpceg9TFVJ3FZO+eijCLG8vpw2c0Bn5IHnYOUZ62uqPZnrBRVxj+7yt9b5YqiK1Ii7Lh1YuS1ql+Dq7c3ouOXioEiGShhNNvvVBPGn/86dB2B2hsMb5R1pn+SKyrzIi7+0QSg+oAaIuwK7FVGxYWHAJk4Ia8+oMr1u4KHilxeF30zfQHbygbkyFegsBFu+PiaXUIIfCWzmOatAafmDDoHKcteHR8f7p7GZBEUVsY3tAJQmKEK7/aWRlblnGjCfouFEE8IIc4E+BNVIE8IcZ8Q4pgQ4tjw8PqGPcSCddGzdg+Eribz4z8urlWdwRBC5RBPdKsikPqD4d8TBT6f5NzAFFdUJv8jY1JT8zrwedQC5Xo58zMVUjMytTAa0rLUv6ftqaCHXBq/hMfnYVfRrvDns59W6ZkH/ijoSLesKuXpDnVOrdk36BxcMyPT0TNFcVUO5g1wPiZdKsSU7KX5fsLeESnlbVLKKwP8+WU0F5JSfktKuV9Kub+kZONzlk1ZWZjz83EPRijkdjsiLQ1zofEl8RHR8iYou1L9bPBCZ/fYLE6Xl11ayNdH/UEVJz/z8/WdZ8quYu1XbnC2yuU03wZDryl7AvDayGsAkeWQX3oUELD3XUEPKa+x4TG5GOiYWLV91j3L6PzoqmIg6ZMM98xQUrcxn9mP7/s4VxRdwcFKY52oeLGlnqstFRV4Fif+hMNtH8BaUZG4+JjJBB96DN7zY6gw9vHu7IDyNq6ozA9zpCYk6bmw8y1KyGPo6b3E2QcAqdIOE0nT7er10qMBd58ZOUNhRmFk0+T7XlE57yF6A20v2o4ju4eeNseq7f4+5DV5y6m/0+PzuOY8cSvNv5wriq/gR3f/aNViazKz3vTDtwoh+oDrgYeEEL8xxqz4YC0vxz0YoZAPDGBJRFhlJWnZKl5uMBcHpzGbxOaffL8RXPUeWJhcjHHHyJmfqmrR4gQ/xpddocbGvfbTgLtPOk6yp3hPeOfG3xsoTO/8faX7GMhvY6rPxdz0cmFQoGHHk0PqF6XR4902C+vNWnlASlktpUyXUpZJKY1XHQOxVJQvZaOEw90/gLVqA3PIN5CLg9PUF2WRYY1Pb+QtRf1Nqq3tq/8Z2/tH2qD/OFz5NmPtigUh1OSq7udgcnXxXO90L73TvVxXGUG/n9F2VSgVRsjLs8txVg2CFHSeXu5N7i86Wink40NqQdkW52KgVGVLhVasFZV4JyfxzYbOMvDNz+MdGSFtswr50DQ7ynV83BBMJtj7blXEFUs3xBPfVb3F977bcNNiwp81c9kszxcGVJXjDZUR1DP4OynWhY8vN26rxJkxQddlQl6QXrAqf3tiaBZrhjkuwyQ2A1tMyFVsL1x4xT2gvpCb0SOfdXnoGZvdmn3E48VV71Y52Kd+FN37PAvKk295A+QmSa+bkhbV16dzdY3fCccJSjNLqc+rD3+O9qfUU0oEhU0N+Q105Z+l7+I4Pq8PULnqK+PjABNDTgrKslIipzsRbDEhjywF0d2vRkxtRiFvdziREraX6fi4YRQ2Ku/z+HfBG3oO5SrO/wpmR+GaP4ibaVEjhMqS6np2VTfEzslOmgqawgup163aFmy7JaIMnJrcGvrzL+Ke9zLUNQ1Ax2THUr8TP2MDTh1WCcHWEvJFYfYLdTDc/ZvXI+8YUdVxeqHTYK77iMr7/8HbYTqCBXXniBokYquDxlvib1801B+EabuqYYClGZYRDVjoPKIWf5sjWy6rzaulP0+NVes5N8rkwiQjcyOr5oDOjC/gnHRRWqvDgcHYUkJuKS0Fi2UpdBIMd38/WK1YEpDvHm86hlU/59oi7d0YSssb1cJnx9OqIjcUUwNw/70w2Qv3/JPhVbvrZmnAtGoa5Zh1MOeZW+MlB+TcL9W0q6bXR3SpmtwaFqyzmCoW6Hx1mI7JDgAabcvXGupS6bJlDVrIg5Fkn6D4IsxmlYIYziMfGMBaXo4wb76sjs4RJ9UFmaRbNt+/LaGYzPD7v1aLhad+CB6XCk1cfGT1PEz7KfjGARi+BO/8gRrqkGyUXgGWTDUnFpbENaxH7vPBxYeh+Q6wRtZRMz89n7y0PGaqBxjtd3KxU7W1XflLY6hzCpNZxHUqUKqz5QY0Wisrw3vkAwMb3/UwAk70jPO1J1r54r1XUlMYm0fdOeKkoVh/IeLGNX+gYt/3vxU8cyq1sPoAHP6MijuffUB5rB98RMXWkxGzRQ22WGw90D3VDRB+odP+KjiHYftdUV2uLq+OjvRTXEkD9nPTpFvTV/UAH+mboagqB4tOlw3KlvLIQcW9U1XIXR4fRy4N0z0aW5MmKSWdI04ai+PfdGjLsu1WqL5W5WL3H4e8ahWi+MHb4OjXAKkm/SSriPup3q+eHjwLDDmHsJgsFGWGGXnY+jggIg6r+GmyNXHOfYr8kkzcvWlL/cD9jA864966NtXZkh65Z2gI6XIh0tbmpEqXC4/DsZThkkz4B0AMTMRWDj4y42JmwUO9jo/HDyHg7d9Ww4yrr4WcUtUa9sKv4bbPQ2Zh8sXEA1G9H553weAZhueGKcksWSWuAWl9TL0vO7oe99ts23ig7QFKmrMYe6mQ0gPLqZiueQ8zYwtxndG5GUiBT5SxpNXWgJS4gsTJ3UNDICXWquTzyMvyMhACBiZjE/LuUScAddojjy+2Wrj6/WrWZlYhtNwF93xDCVwqiDisWvAcmh2iJCvMwr9zRD2BNN8R9aWabYutCWqcmL1WaqaX55JOOhZL83XqYUhS5FNlHNYa1Us52ICJ5fa1ySfkaRYTJTnpMXvkXYshmfoiLeSaMORVqrBQ+5M4Zh1rZmeuoe1JQKpBzlHSVKCGHTuKOnCZ5insr1/aNz6onA+dQx6aLSfkabWqYszVE3jAxFJVZxIKOUClLZOBibWN+COhe9SJ2ST0jE5NZFz9fmh9DMfMIKVZpaGP7T+mFnHLo+/UWZJZQn56PsdGXqGr8AyW7gJ8PlWMNDbgRJgEthIt5KHYckJuLipCZGXh6g3ikff3gxAbP+ItQiptGTF75N2js1TZMvVUIE1kvO4+nOY0nN45SjLDhFYGz6juiTGEjoQQNNuaOTpwlO6CM8h5E44uNWzC0T1FUVU2Zqv+zIZiy90dIQRpNTW4g3nk/f1YSksxBVgITQYq8zMZmJwLOOcwHN2jTur0QqcmUjILcJSrWbEhPXIpYejsuqYbNRc0s+BdoC//IgjoOTeGlBJH9zSl9boQKBxbTsgB0urqWOjsCLjP3d+f1KX5FbZM5t0+xmfdUb+3Z2yW2hjzzzVbE0epWogsywiRiTLZq8ry/ROtYsA/Um3BOktRbRbdZ0aZdMyxMOuhbIOmAqUyW1LI03e04O7uwTvjXLPP3d+ftPFxgMr8DCD6FMSpeTfjs24t5JqocOQrp6Z0djz4Qf0n1Gt5BCPggtBSoDJV3tz4ZpquKsfRNUXHq2q2b2m97tQZji0p5Bk71ePiwsULq7ZLjwf34GDSe+QA9snoFjx7FjNWtJBrosGRXQBA6dCF4Ae1PwVpuaoaNEZ2F+/mX27/F75w8Ats26fi8S/+oh2L1URhhc6yCsfWFPJdagr4/Lnzq7Z7hobA603KHHI/lTblkdujzCXvHVNCHmtpv2ZrMiwXyJGQ1fV84AOkVELeeBjM1pivI4TghsobsJqsFJRnU1SVg5RQXJOLybwlZSoqtuQdspSWYi4qYv7cuVXbl1IPK5JXyIuz07GaRdQpiD2LQq67HmqiwTHroMSSDd1HVSOwy7GfUjHyGPLHQ9F8QC2upmdvueLzmNiSQi6EIHP3buZOnVq13b04zzOZPXKTSVCenxG1R949Nosty0peRuxek2brMTQ7RGl2GbhnlWhfzonvgyUDdt1r6HW3X6umeV1xY/J+F5OJLSnkAJlXXYWrowPvxMTStqWqzvLyBFkVGRX5mUuhkkg5OzDF9lK9aKSJjuHZYUr9vcF7X1y9c8YBp/9LiXimzdDr5hZm8Mf/fAsNezffTIB4sKWFHGDu9OmlbW67HXNBAaas5A4/HKgv4ETPBN892hnR8fNuL2f7J7m6riDOlmk2Ez7pU0KeVwcF9dBzmZA/+3/AMw+HPhWX6+v5nJGzdYV8t8p5XRknd9sHkrLr4eX8+e0ttJTl8uQFR0THv9Y/iccnubrWFl/DNJuKQecgHulRvcHrDkL706rwx8+Fh1Xv8eKmxBmpAbawkJuyszEXF6+aFuSx27FUJr+Qm02CbaXZ9EeYS/5Sh5pQs69We+SayLk0fgmA7QXb4ebPQnoOPPIXMHwR/uk6mOxRg5o1CWfLCjmoRU1XXx+ghi64B+xYy5NfyGGxVH8ifKm+lJIHTw1wTV0BJbnpG2SdZjPQOq6GIjfZmsBWA3veqcIrx74Dw4upu7XXJdBCjZ8tLeRpVVW4+1XKoW9qCp/TmdRVnSupKoisVP/i0DSXhma4d1/yFjlpkpNL45eoyqkiJ21xNGDjzeBzqwVOAHM6lMVezakxji0t5Naqatx2O9LrXU49TBEhr1ys8OwfDx1eea51BIDbd4bpJ63RXEbreOvy0AeAuhtUquHcuBog8Redar6nJuFscSGvArcbj8OxXAyUxDnkK/H3FA8XJ3+la4zawizKF3u0aDSR4Pa56Z7qXhr6AIA1E3a+Wf1sq4U0XTqfLGxtIa+uBqDvY39C3x9/VG1LEY/cL+SPnLHj9QWOk0speaVrnAP1hRtpmmYT0DvVi0d6aMy/bEj06z6iXhtv2XijNEHZ0kKesUN1XJs/u5xSZS5MDdEryE7jj25q4JevDnCkdTjgMf0Tc4w5XezTaYeaKOmYVG2eG22XCXn1NfCZHtjxpgRYpQnGlhZyS3HxklfuJ5WKED52i4pfXhqcDrjf31+lQQ9b1kRJ+0Q7AA15DWt3ZuRDCn1PtgLrEnIhxN8LIS4IIU4LIR4QQtgMsmvDyNy7YsagJbUWbvKzrBTnpNPmmAm431/Gr1vXaqKlY7KDyuxKsqz6s5MKrNcjfxy4Ukq5B7gEfHb9Jm0s2Tdcj8jMpO6H/0nTbx5NtDlR01SaTftwYCHvGZvFYhJU6IVOTZS8oeEN3LfnvkSboYmQdbmgUsrHVvz1ReDt6zNn48l/61vJufVWLAWpWfW4rSSHX5+2I6VcExbqGZujqiATi+7nrImSm2tuTrQJmigw8hv+QeCRYDuFEPcJIY4JIY4NDwdenEsEwmRKWREHJeSTc25Gnat7RXeNOPnVqQEdVtFotgBhhVwI8YQQ4kyAP/esOOYvAQ/wg2DnkVJ+S0q5X0q5v6REt6Y0ivpiJdQ9l7W1/atfngGgpUy3rtVoNjthQytSyttC7RdCfAC4G3i9DNf4Q2M4fo+7d2yWq1c0xeoadbK3xsan79qRKNM0Gs0Gsd6slbuAvwDeIqWMbtKBxhCqCxY98tHl2+/x+rBPzHNjUxFpFh0f12g2O+v9ln8DyAUeF0K8KoT4pgE2aaIgw2qmLC99VWhlcGoej08uibxGo9ncrDdrRXeUTwJqC7NWCXnvmOq/UqOFXKPZEujn7k1AzWVC3jeufq4uyEyUSRqNZgPRQr4JqC7IYnBqHpfHB0Dv+BxCLLe61Wg0mxst5JuA6oJMpAT7pAqp9Iw6qczP1AudGs0WQX/TNwH+EErf4pCJztHZpfxyjUaz+dFCvgnwL2r6Y+Pdo07qi3THQ41mq6CFfBNQnp+BSSiPfGLWxcSsWwu5RrOF0EK+CbCaTVTkZ9I3PkfXYmFQve5BrtFsGbSQbxJqC7PoHHEuDZnQwyQ0mq2DFvJNQkt5Lq1D0zzXNkJxThrbSrSQazRbBS3km4TtZbk4XV4ePDXAwabilBpZp9Fo1ocW8k1CS3nO0s+HmnWbYI1mK6GFfJPQvKLv+N17KxJoiUaj2WhSa9qwJih5GVY+dWcL128rIt1iTrQ5Go1mA9FCvon46C26GaVGsxXRoRWNRqNJcbSQazQaTYqjhVyj0WhSHC3kGo1Gk+JoIddoNJoURwu5RqPRpDhayDUajSbF0UKu0Wg0KY6QUm78RYUYBro3/MKKYmAkQdeOFG2jMSS7jcluH2gbjcBI++qklGuaKSVEyBOJEOKYlHJ/ou0IhbbRGJLdxmS3D7SNRrAR9unQikaj0aQ4Wsg1Go0mxdmKQv6tRBsQAdpGY0h2G5PdPtA2GkHc7dtyMXKNRqPZbGxFj1yj0Wg2FVrINRqNJsXZ9EIuhOgSQrwmhHhVCHFscVuhEOJxIUTr4mvBBtv0HSGEQwhxZsW2oDYJIT4rhGgTQlwUQtyZIPs+L4ToX7yPrwoh3pgo+xavWSOEeFoIcV4IcVYI8aeL25PiPoawL2nuoxAiQwjxshDi1KKNX1jcnhT3MIyNSXMfF69pFkKcFEL8evHvG3sPpZSb+g/QBRRftu0rwGcWf/4M8L832KZDwNXAmXA2AbuAU0A60AC0A+YE2Pd54JMBjt1w+xavWwFcvfhzLnBp0ZakuI8h7Eua+wgIIGfxZyvwEnBdstzDMDYmzX1cvO6fA/8J/Hrx7xt6Dze9Rx6Ee4DvLf78PeDejby4lPIIMBahTfcAP5JSLkgpO4E24NoE2BeMDbcPQEppl1KeWPx5GjgPVJEk9zGEfcFIxP+zlFLOLP7VuvhHkiT3MIyNwdhwG4UQ1cCbgH+7zI4Nu4dbQcgl8JgQ4rgQ4r7FbWVSSjuoLxxQmjDrlglmUxXQu+K4PkILQjz5mBDi9GLoxf+omHD7hBD1wD6Ut5Z09/Ey+yCJ7uNiSOBVwAE8LqVMunsYxEZInvv4NeDTgG/Ftg29h1tByA9KKa8G3gB8VAhxKNEGRYkIsC0ROaP/DGwDrgLswP9Z3J5Q+4QQOcDPgD+TUk6FOjTAtrjbGcC+pLqPUkqvlPIqoBq4VghxZYjDk8nGpLiPQoi7AYeU8nikbwmwbd32bXohl1IOLL46gAdQjzFDQogKgMVXR+IsXCKYTX1AzYrjqoGBDbYNKeXQ4hfKB/wry4+DCbNPCGFFieQPpJQ/X9ycNPcxkH3JeB8X7ZoAfgvcRRLdw2A2JtF9PAi8RQjRBfwIuFUI8R9s8D3c1EIuhMgWQuT6fwbuAM4ADwIfWDzsA8AvE2PhKoLZ9CDwLiFEuhCiAWgGXt5o4/wfykXeirqPCbNPCCGAbwPnpZT/sGJXUtzHYPYl030UQpQIIWyLP2cCtwEXSJJ7GMrGZLmPUsrPSimrpZT1wLuAp6SU72Wj72G8V3MT+QdoRK0QnwLOAn+5uL0IeBJoXXwt3GC7foh6HHSjfkN/KJRNwF+iVrcvAm9IkH33A68Bpxc/jBWJsm/xmjeiHklPA68u/nljstzHEPYlzX0E9gAnF205A/zPxe1JcQ/D2Jg093HFdW9mOWtlQ++hLtHXaDSaFGdTh1Y0Go1mK6CFXKPRaFIcLeQajUaT4mgh12g0mhRHC7lGo9GkOFrINRqNJsXRQq7RaDQpzv8PUe/0QXbrHDIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each extracted shapelet (in descending order of quality/information gain)\n",
    "for i in range(0, min(len(st.shapelets), 5)):\n",
    "    s = st.shapelets[i]\n",
    "    # summary info about the shapelet\n",
    "    print(\"#\" + str(i) + \": \" + str(s))\n",
    "\n",
    "    # overlay shapelets\n",
    "    plt.plot(\n",
    "        list(range(s.start_pos, (s.start_pos + s.length))),\n",
    "        train_x.iloc[s.series_id, 0][s.start_pos : s.start_pos + s.length],\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-19T14:34:05.012159Z",
     "iopub.status.busy": "2020-12-19T14:34:05.011589Z",
     "iopub.status.idle": "2020-12-19T14:35:17.234842Z",
     "shell.execute_reply": "2020-12-19T14:35:17.235370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0774919986724854\n",
      "3.094931125640869\n",
      "3.2343239784240723\n",
      "Results:\n",
      "Correct:\n",
      "\t105/242\n",
      "\t0.43388429752066116\n",
      "\n",
      "Timing:\n",
      "\tTo build:   65.61358594894409 secs\n",
      "\tTo predict: 6.453415870666504 secs\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sktime.datasets import load_osuleaf\n",
    "\n",
    "train_x, train_y = load_osuleaf(split=\"train\", return_X_y=True)\n",
    "test_x, test_y = load_osuleaf(split=\"test\", return_X_y=True)\n",
    "\n",
    "# example pipeline with 1 minute time limit\n",
    "pipeline = Pipeline(\n",
    "    [\n",
    "        (\n",
    "            \"st\",\n",
    "            ContractedShapeletTransform(\n",
    "                time_contract_in_mins=time_contract_in_mins,\n",
    "                num_candidates_to_sample_per_case=10,\n",
    "                verbose=False,\n",
    "            ),\n",
    "        ),\n",
    "        (\"rf\", RandomForestClassifier(n_estimators=100)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "start = time.time()\n",
    "pipeline.fit(train_x, train_y)\n",
    "end_build = time.time()\n",
    "preds = pipeline.predict(test_x)\n",
    "end_test = time.time()\n",
    "\n",
    "print(\"Results:\")\n",
    "print(\"Correct:\")\n",
    "correct = sum(preds == test_y)\n",
    "print(\"\\t\" + str(correct) + \"/\" + str(len(test_y)))\n",
    "print(\"\\t\" + str(correct / len(test_y)))\n",
    "print(\"\\nTiming:\")\n",
    "print(\"\\tTo build:   \" + str(end_build - start) + \" secs\")\n",
    "print(\"\\tTo predict: \" + str(end_test - end_build) + \" secs\")"
   ]
  }
 ],
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   "language": "python",
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  "language_info": {
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